@Article{cmc.2020.010802,
AUTHOR = {Yun Tan, Jiaohua Qin, Hao Tang, Xuyu Xiang, Ling Tan, Neal N. Xiong},
TITLE = {Privacy Protection for Medical Images Based on DenseNet and  Coverless Steganography},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {3},
PAGES = {1797--1817},
URL = {http://www.techscience.com/cmc/v64n3/39460},
ISSN = {1546-2226},
ABSTRACT = {With the development of the internet of medical things (IoMT), the privacy 
protection problem has become more and more critical. In this paper, we propose a 
privacy protection scheme for medical images based on DenseNet and coverless 
steganography. For a given group of medical images of one patient, DenseNet is used to 
regroup the images based on feature similarity comparison. Then the mapping indexes 
can be constructed based on LBP feature and hash generation. After mapping the privacy 
information with the hash sequences, the corresponding mapped indexes of secret 
information will be packed together with the medical images group and released to the 
authorized user. The user can extract the privacy information successfully with a similar 
method of feature analysis and index construction. The simulation results show good 
performance of robustness. And the hiding success rate also shows good feasibility and 
practicability for application. Since the medical images are kept original without 
embedding and modification, the performance of crack resistance is outstanding and can 
keep better quality for diagnosis compared with traditional schemes with data embedding.},
DOI = {10.32604/cmc.2020.010802}
}